Wrist‐worn accelerometers: Influence of decisions during data collection and processing: A cross‐sectional study

Abstract Background and Aims Accelerometers collect data in an objective way, however, a number of decisions must be done during data collection, processing and output‐interpretation. The influence of those decisions is seldom investigated, reported, or discussed. Herein, we examined the influence of different decisions on the outcomes: daily minutes of moderate‐to‐vigorous physical activity (MVPA), inactivity and light physical activity (LPA). Methods In total, 156 participants wore an accelerometer (ActiGraph wGT3X‐BT) on their nondominant wrist for 7 days. Data collection was conducted from February 2017 to June 2018. Data was processed using the R‐package GGIR and default settings were compared to by‐the‐literature‐suggested options. The output was examined using paired t‐tests. Results When comparing two commonly used MVPA‐cut‐points, default and Hildebrand et al. we found a marginal difference (0.4 min, 1.0%, p < 0.001) in MVPA/day. When no bout criteria for MVPA/day was applied, MVPA/day was twice as high as bouted MVPA/day. Further, when we changed the epoch‐length from 5 to 1 s, statistically significant changes were seen for MVPA/day (−6.6 min, 19%, p < 0.001), inactivity/day (−22 min, 3.0%, p < 0.001) and LPA/day (28 min, 81%, p < 0.001). Conclusion Decisions made during data processing of wrist‐worn accelerometers has an influence on the output and thus, may influence the conclusions drawn. However, there may be situations when these settings are changed. If so, we recommend examining if the variables of interest are affected. We encourage researchers to report decisions made during data collection, processing and output‐interpretation, to facilitate comparisons between different studies.

Technical innovations have made objective assessments of physical activity possible and advanced research within the field of public health and epidemiology.Wearables, such as accelerometers, are available on a larger scale than before and feasible to use in both large-scale epidemiological studies as well as in standard health care.
The early versions of accelerometers were hip-worn and uniaxial. 1Data was processed simultaneously as it was collected, using brand-specific methods, 2 which resulted in a lack of awareness about the actual processing algorithms utilized. 3Today's accelerometers are triaxial. 1Raw data is stored in the device, and data processing is done afterwards. 1 Further, wrist-worn, instead of hipworn, accelerometers are becoming more common, since this increases compliance and enables sleep-tracking. 1,2A wrist-worn accelerometer collects more acceleration-data compared to a hipworn, though some of the additional data collected is only related to random wrist movements. 1Therefore, data collected using wristworn accelerometers must be processed differently from hip-worn accelerometers.
Even though accelerometers collect data in an objective way, a number of decisions must be made by the researcher, both during data collection, processing and output-interpretation. 3This includes decisions regarding: sampling frequency, epoch-length, definition of and how to handle nonwear time, cut-points for the physical activity levels of interest, and which of the possibly hundreds of outputvariables to use. 1,4Since these decisions can have a major influence on the final output, it is important that researchers apply them in a well-thought-out way.However, the field of accelerometry is not yet standardized and many do not report or justify the reasons for the key methodological choices made during data collection and processing when presenting their final results. 5 collected accelerometer-data in a clinical study using a wristworn accelerometer, but found no protocol or exhaustive guide for processing and analyzing this type of accelerometer-data.
Therefore, the aim of this paper is to evaluate the influence of different decisions during data collection, processing and outputinterpretation, using wrist-worn accelerometers (ActiGraph wGT3X-BT).Also, by illustrating the influence that these decisions can have on the output, we want to highlight the importance of reporting these.Here we present results from analyses of data from accelerometer measurements performed within the trial, where we used default settings and compared them to commonly used options from the literature.

| Study design
This study is based on baseline data from a randomized controlled trial comprising 181 participants with diabetes type 2, recruited from February 2017 to June 2018 at primary care centers and one specialist center in Stockholm, Sweden.Inclusion criteria were: having a diagnosis of diabetes type 2, at least 18 years old, ability to communicate in Swedish, and having and being able to use a smartphone.Exclusion criteria were: not being able to walk.Details on the design can be found elsewhere. 6Herein, we used accelerometer-data from baseline measurements, that is, before the intervention took place.The study was approved by the Regional ethical review board, Stockholm, Sweden (2016/2041-31/2; 2016/ 99-32; 2017/1406-32), and performed in accordance with the declaration of Helsinki.All participants provided written informed consent before enrollment.

| Measurements
Participants were asked to wear the ActiGraph wGT3X-BT accelerometer for all hours on their nondominant wrist, starting at 4 p.m. the same day, and removing it at 8 a.m. after seven full days.Data was collected at a sampling frequency of 80 Hertz (Hz), that is, 80 measurements per s. 7  GGIR consists of five parts (part1−part5).The first part includes averaging raw acceleration-data over epochs, and aggregation through application of Euclidean norm minus one (ENMO).Negative values are rounded up to zero. 8Next, an autocalibration function adjusts for calibration errors and replaces unreliable signals. 9,10This is done using local gravity retrieved by the defined time zone of the measurement.Inaccurate data is replaced with averaged data from the same time the other measured days.Further, data collected before the first and after the last midnight is excluded to obtain seven complete days, using the setting strategy. 10nwear time was defined using default settings, that is, in bouts of at least 1 h (four consecutive 15-min blocks). 8The nonwear time was replaced with the averaged activity the same time-period the other measured days, similar to the autocalibration function. 8With GGIR you can chose to either derive data from wake-up to wake-up, or midnight to midnight. 10We defined the time-window as midnight to midnight, to retrieve data summarized over 24 h.Output on physical activity was produced in two steps, that is, GGIR part2 and part5. 8The output was summarized in absolute numbers as milli gravity (mg, 1 mg = 0.00981 m•s −2 ), and as time spent performing activities of different intensities, by using the average acceleration per epoch. 8The output on moderate-to-vigorous physical activity (MVPA)/day was produced in both part2 and part5, while output for inactivity/day and light physical activity (LPA)/day was only produced in part5.

| Accelerometer-data processing using GGIR
To capture not only sporadic movements but also sustained activity, we obtained data on bouted variables for MVPA, LPA, and periods of inactivity.For MVPA, bouts for sustained activity was defined as at least 1 min with an 80% criterion, 11 that is, 80% of the included epochs had to be equal to or above the MVPA-cut-point. 8rther, inactivity was defined as bouts of at least 10 min with 90% criterion and LPA as bouts of at least 1 min with 80% criterion. 8fault GGIR cut-points was applied for inactivity (<40 mg) and LPA (40−100 mg). 8Due to bout criteria and weighting of variables, the output did not add up to 24 h.

| Testing of settings
Default settings were identified and compared to one or two commonly used options from the literature.These settings were chosen after the data collection, but before the data processing and analysis.Settings were cut-points for MVPA: 100, 8 100.6, 12 or 110 mg, 13 epoch-length: 1, 12 5, 8 or 60 s, 1,14 the definition of a valid day: 12, 14, or 16 h, 1,15 and valid week: 3 or 4 days. 1,15Further, we used data extracted from GGIR part2 or part5, variables with a bout criterion or not, and weighted or plain variables.Default settings were applied to all parameters except the one that was tested.Our main outcome was minutes of MVPA/day, but for further comparisons, minutes of inactivity/day and LPA/day were also extracted.

| Statistical analysis
Descriptive statistics are presented as mean values with standard deviations (SD).Two-tailed paired t-tests were used to examine any difference between output of the default setting and each alternative setting.Results are presented with mean values and p values.Spearman rank's test for correlation examined associations between number of valid days and minutes of MVPA/day, inactivity/day and LPA/day, respectively.These results are presented with the correlation coefficient (r) and corresponding p value.Participants that fulfilled the default inclusion criteria were included in the analysis, that is, had used the accelerometer for at least 16 h for 4 days (including at least 1 weekend day).All reported p values were twosided and p < 0.05 were considered statistically significant.Statistical analyses were performed using STATA 14.2.

| RESULTS
In total, 156 participants provided accelerometer-data that fulfilled the inclusion criteria for accelerometer-data processing.Participants were 66% male and had a mean age of 60 (SD 11) years.When using default settings for accelerometer-data processing, an average of 33.9 (SD 26.5) min were spent on MVPA/day, while 698 (SD 179) min were spent inactive/day and 34.7 (SD 25.9) min were spent on LPA/day.Our decisions based on our results are also summarized in Table 1.Table 2 comprises practical tips gathered when accelerometer-data was collected and processed within this trial.
Figure 1 shows an overview of the process of collecting, processing and interpreting accelerometer-data.

| MVPA-cut-point
We found that MVPA/day was statistically significantly lower by on average 0.4 min (1.0%, p < 0.001) when the cut-point was changed from default 100 mg 8 to 100.6 mg Hildebrand et al. 12 When the suggested cut-point for the dominant wrist 110 mg by Migueles et al. 13 was applied, minutes of MVPA/day was statistically significantly lower by on average 6.2 min (18%, p < 0.001) in our data.

| Valid day
A valid day is commonly defined as 16 h, that is, two thirds of the day.In our data, the sample size increased by 3.0% (n = 5) and 5.0% (n = 8) when we changed the definition of a valid day from 16 h to 14 h and 12 h, respectively.Furthermore, MVPA/day became 0.08 min (0.28%) respectively 0.16 min (0.48%) higher, although these changes were not statistically significant (p = 0.35 and p = 0.15).LPA/day did not change statistically significantly when the criteria changed from 16 h to neither 14 h (−0.21 min, 0.61%, p = 0.27) nor 12 h (−0.47 min, 1.3%, p = 0.08).For inactivity/day, small, but statistically significant, changes were seen when the definition of a valid day changed from 16 h to 14 h (−4.0 min, 0.57%, p = 0.009), and to 12 h (−4.8 min, 0.68%, p = 0.005).

| Valid week
A valid week is often defined as 4 days, including at least 1 weekend day. 1,15When we changed the definition of a valid week EKE ET AL.
| 3 of 8 to 3 days (including at least 1 weekend day), our sample size increased by 1.3% (n = 2).Those who did not fulfill the default criterion for a valid week, compared to those who did not, had on average 8.4 [SD 6.6] min lower MVPA/day, but this was not statistically significant (p = 0.21).Further, we found no correlation between number of valid days and minutes of MVPA/day (r = 0.10, p = 0.20), inactivity/day (r = −0.14, p = 0.06) or LPA/day (r = −0.007,p = 0.93), respectively.
T A B L E 1 Summary of decisions during data collection, processing and output-interpretation.T A B L E 2 Practical tips. 1.
To facilitate good compliance-equip the accelerometers with adjustable, elastic wrist-bands, since the wrist circumferences may vary.

2.
When extracting the raw data from ActiGraph-accelerometers, keep all GT3X-files in the same folder.This enables ActiLife to extract all files simultaneously.

3.
Do not keep dates and timestamps when the files are extracted from ActiLife as GGIR cannot handle these.(The start date and start time is already automatically kept elsewhere in the file header, i.e., no information is lost.)

4.
If you are not familiar with GGIR or R/RStudio-see the tutorials below.

5.
If you encounter problems or have questions regarding your data processing-join the google group "R-package GGIR" where developers and users can connect.

6.
If you are using ActiGraph-accelerometers and are unsure whether there is wear-time on a certain file, and you are not that familiar with GGIR, it can be checked using ActiLife.Use the tabs "wear time validation" and "scoring." Accelerometers collect physical activity data in an objective way; however, decisions during data processing can influence the output, as was shown in our data.For example, minutes unbouted MVPA was twice as high as when an 80% 1-min bout criterion was applied.Historically, unbouted MVPA-output has been used, but nowadays a bout criterion as in for example Whitehall II study 11 is becoming more common.Further, MVPA/day were doubled when it was retrieved from GGIR part2 instead of part5, which indicates that the MVPA-algorithms differs considerably.To our knowledge, part5 is the most commonly used output, for this type of study.We also found that the epoch-length can affect the output.For example, minutes of MVPA/day and inactivity/day became lower when we changed the epoch-length from 5 to 1 s, while LPA/day became higher.
It may be appealing to compare the output from GGIR with the historically common output from ActiLife to examine differences between the methods.This must however be made with caution as the default settings vary between the methods, such as epoch-length and bout criteria.Nonetheless, Montoye et al. 14 developed cut-points for the nondominant wrist to apply when using ActiLife for data processing, and included a comparison between the methods.They found that using the output from GGIR lead to a lower level of misclassification, compared to when using the output from ActiLife.
F I G U R E 1 An overview of the process of collecting, processing and interpreting accelerometer-data collected using a wrist-worn accelerometer.LPA, light physical activity; MVPA, moderate-to-vigorous physical activity.
There are situations when the default settings may be replaced with alternatives.For example, other cut-points may be considered for persons with disabilities that affect their movement pattern.We only included adults without disabilities.Our suggestions and recommendations are thus primarily for this type of population.
Further, the epoch-length should be the same as when the applied cut-points were developed, that is, if the cut-points are changed, the epoch-length should be changed accordingly. 1

| Placement of the accelerometer
We used the nondominant wrist, since it is the most common placement and therefore facilitates comparisons between studies. 2 According to some authors, movements from the dominant wrist are on average 10% higher compared to movements from the nondominant wrist, 13 while others mean that there is no difference. 16ing the ActiGraph GT3X+, Buchan et al. 17 found an 8.5% higher average acceleration of the dominant wrist, compared to the nondominant wrist.This is in accordance with results from the consumer-based activity monitor Fitbit. 18There is however no consensus on what wrist provides the best estimate of habitual total activity over several days. 3Even though the cut-points for the dominant wrist are not supposed to be applied for accelerometerdata collected using the nondominant wrist, our results illustrate the differences between the cut-points, and stress the importance of instructing the participants on what wrist to wear the accelerometer.

| Sampling frequency
As sampling frequency must be decided a priori, we decided to use 80 Hz in accordance with the NHANES-study. 2This is a compromise between high sampling frequency and storage capacity.Frequencies between 30 and 100 Hz are available when using ActiGraph wGT3X-BT, 7 in contrast to previous versions that automatically collected data at 10 or 30 Hz. 19 Previous studies have shown that sampling frequency affects the output when analyzed with ActiLife, especially for high intensity activities. 1,19However, data collected at a variety of sampling frequencies can be processed using GGIR, since data is normalized by averaging into epochs as the first step of data processing.

| MVPA-cut-point
We used the default MVPA-cut-point for the nondominant wrist (100 mg). 8This cut-point or the cut-point developed by Hildebrand et al. 12 (100.6mg), are usually recommended. 1However, these two cut-points may be confused; as the former is often applied, but the latter is commonly referred to.Nevertheless, our study indicated that this may not be a major problem, since the difference in MVPA/day was marginal.However, the latter cut-point is developed using 1 s epoch-length compared to the default 5 s epoch-length.When we changed the epoch-length accordingly, we found statistically significantly lower MVPA/day by almost 20% (6.6 min), which can be considered clinically significant.Further, da Silva et al. 21also used the default cut-point when processing accelerometer-measurements from almost 9000 participants in three Brazilian cohort studies.The argument used was that it was within the "range of acceleration values corresponding to walking" and "a round number so as not to give the impression of over-precision." Even though cut-points are applied post data collection, it may be worth to consider MVPA-cut-point already beforehand, since a general recommendation is to keep all other parameters in accordance with when the cut-point was developed. 1For example, if a MVPA-cut-point was developed using an accelerometer worn on the nondominant wrist and a sampling frequency of 100 Hz, these parameters are recommended to be constant when applying the cut-point again. 1

| Definition of a valid day, valid week and wear-time protocol
The number of valid days needed for an individual's physical activity assessment depends on requirements on data accuracy, which in turn, depends on compliance as well as day-to-day variation within the individuals.If all participants wear the device continuously, there is no need to have a longer wear-time protocol than what is defined as a valid week, though some extra time is often added.The measurement period normally varies from 1 day up to several weeks, and a common strategy is to ask the participants to wear the accelerometer for seven consecutive days, to include both weekdays and weekend days. 1,15Further, compliance increases if the participants are instructed to wear the accelerometer continuously, instead of only during waking hours. 1 Data processing to convert raw accelerometer-data into output on physical activity was performed using the open-source R-package GGIR 4 version 2.0-0 (released 2020), R version 3.6.1 and RStudio version 1.2.5019.Raw data from each accelerometer was downloaded to a GT3X-file by using the ActiGraph's brand-specific program ActiLife version 6.13, and loaded into R/RStudio.
7hus, it important to understand and consider the normal range of human movement frequencies.With this in mind, and since we do not know what processing methods the future holds, the best available recommendation is to use the highest possible sampling frequency, that is, 100 Hz if using the ActiGraph wGT3X-BT.1Alowerfrequency may be considered if the measurement period is very long (e.g., several weeks), due to storage limitations.7 this strengthens the argument that output collected and processed at different sampling frequency should be compared with caution.Sampling frequency has implications far beyond data storage and downstream calculations.Fundamentally, the sampling frequency determines the range of movement frequencies that can be captured.